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Visibility Improvement in Hazy Conditions via a Deep Learning Based Image Fusion Approach

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Advances in Computing and Data Sciences (ICACDS 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1440))

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Abstract

Foggy weather prominently causes degradation in visibility due to the scattering of the atmospheric particles. Consequently, there arises a problem in identification of the precise object features by the human eye as well as the machine based computer vision systems. To encounter such situations, various adept mechanisms are required. The proposed scheme attempts to encompass the deep learning approach for the amalgamation of the RGB and Infra-red imaging in order to improve the vision quality of the hazy images. A fused image is obtained via intelligent conjunction of significant information from both the imaging schemes. Subsequently, the combined image is processed using a Dark Channel Prior algorithm and a bilateral filtering is used to maintain the edge information. Comparative results using various quality parameters including entropy, Standard Deviation, Similarity Index, and Peak Signal to Noise Ratio signifies that the proposed fusion scheme performs better than contemporary single image de-hazing algorithms.

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References

  1. Ministry of Road Transport & Highways, Government of India. https://morth.nic.in/. Accessed 22 Feb 2021

  2. Fan, X., Wang, L.: Image defogging approach based on incident light frequency. Multimed. Tools Appl. 78(13), 17653–17672 (2019). https://doi.org/10.1007/s11042-018-7103-1

    Article  Google Scholar 

  3. Narasimhan, S.G., Nayar, S.K.: Vision and the atmosphere. Int. J. Comput. Vis. 48, 233–254 (2002)

    Article  Google Scholar 

  4. Nayar, S.K., Narasimhan, S.G.: Vision in bad weather. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 820–827. IEEE (1999)

    Google Scholar 

  5. Jin, X., et al.: Infrared and visual image fusion method based on discrete cosine transform and local spatial frequency in discrete stationary wavelet transform domain. Infrared Phys. Technol. 88, 1–12 (2018)

    Article  Google Scholar 

  6. Vanmali, A.V., Gadre, V.M.: Visible and NIR image fusion using weight-map-guided Laplacian-Gaussian pyramid for improving scene visibility. Sadhana - Acad. Proc. Eng. Sci. 42, 1063–1082 (2017)

    Google Scholar 

  7. Sharma, V., Hardeberg, J.Y., George, S.: RGB-NIR image enhancement by fusing bilateral and weighted least squares filters. J. Imaging Sci. Technol. 61, 1–9 (2017)

    Google Scholar 

  8. Wang, X., Nie, R., Guo, X.: Two-scale image fusion of visible and infrared images using guided filter. In: ACM International Conference Proceeding Series, pp. 217–221. Association for Computing Machinery, New York (2018)

    Google Scholar 

  9. Bavirisetti, D.P., Dhuli, R.: Two-scale image fusion of visible and infrared images using saliency detection. Infrared Phys. Technol. 76, 52–64 (2016)

    Article  Google Scholar 

  10. Han, X., et al.: An adaptive two-scale image fusion of visible and infrared images. IEEE Access. 7, 56341–56352 (2019)

    Article  Google Scholar 

  11. Umbgen, F.D., El Helou, M., Gucevska, N., Usstrunk, S.S.: Near-Infrared Fusion for Photorealistic Image Dehazing (2018)

    Google Scholar 

  12. Li, S., Kang, X., Hu, J.: Image fusion with guided filtering. IEEE Trans. Image Process. 22, 2864–2875 (2013)

    Article  Google Scholar 

  13. Liang, J., Zhang, W., Ren, L., Ju, H., Qu, E.: Polarimetric dehazing method for visibility improvement based on visible and infrared image fusion. Appl. Opt. 55, 8221 (2016)

    Article  Google Scholar 

  14. Brown, M., Susstrunk, S.: Multi-spectral SIFT for scene category recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 177–184. IEEE Computer Society (2011)

    Google Scholar 

  15. Anwar, M.I., Khosla, A.: Vision enhancement through single image fog removal. Eng. Sci. Technol. Int. J. 20, 1075–1083 (2017)

    Google Scholar 

  16. Khan, A., Sohail, A., Zahoora, U., Qureshi, A.S.: A survey of the recent architectures of deep convolutional neural networks. Artif. Intell. Rev. 53, 5455–5516 (2021). https://doi.org/10.1007/s10462-020-09825-6

  17. Mathew, A., Amudha, P., Sivakumari, S.: Deep learning techniques: an overview. In: Hassanien, A.E., Bhatnagar, R., Darwish, A. (eds.) AMLTA 2020. AISC, vol. 1141, pp. 599–608. Springer, Singapore (2021). https://doi.org/10.1007/978-981-15-3383-9_54

    Chapter  Google Scholar 

  18. Tyagi, V.: Understanding Digital Image Processing. CRC Press, Boca Raton (2018). https://doi.org/10.1201/9781315123905

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Acknowledgment

The research work was funded by Technical Education Quality Improvement Program (TEQIP-III) under Collaborative Research Scheme project titled: Development of Fusion based Defogging Technique for visibility improvement.

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Correspondence to Asifa Mehraj Baba .

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Singh, S., Baba, A.M., Anwar, M.I., Moon, A.H., Khosla, A. (2021). Visibility Improvement in Hazy Conditions via a Deep Learning Based Image Fusion Approach. In: Singh, M., Tyagi, V., Gupta, P.K., Flusser, J., Ören, T., Sonawane, V.R. (eds) Advances in Computing and Data Sciences. ICACDS 2021. Communications in Computer and Information Science, vol 1440. Springer, Cham. https://doi.org/10.1007/978-3-030-81462-5_37

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  • DOI: https://doi.org/10.1007/978-3-030-81462-5_37

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-81461-8

  • Online ISBN: 978-3-030-81462-5

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